[petsc-users] Questions about setting values for GPU based matrices

[Matthew Knepley]

On Tue, Nov 29, 2011 at 2:38 AM, Fredrik Heffer Valdmanis <fredva at ifi.uio.no
> wrote:

> 2011/10/28 Matthew Knepley <knepley at gmail.com>
>
>> On Fri, Oct 28, 2011 at 10:24 AM, Fredrik Heffer Valdmanis <
>> fredva at ifi.uio.no> wrote:
>>
>>> Hi,
>>>
>>> I am working on integrating the new GPU based vectors and matrices into
>>> FEniCS. Now, I'm looking at the possibility for getting some speedup during
>>> finite element assembly, specifically when inserting the local element
>>> matrix into the global element matrix. In that regard, I have a few
>>> questions I hope you can help me out with:
>>>
>>> - When calling MatSetValues with a MATSEQAIJCUSP matrix as parameter,
>>> what exactly is it that happens? As far as I can see, MatSetValues is not
>>> implemented for GPU based matrices, neither is the mat->ops->setvalues set
>>> to point at any function for this Mat type.
>>>
>>
>> Yes, MatSetValues always operates on the CPU side. It would not make
>> sense to do individual operations on the GPU.
>>
>> I have written batched of assembly for element matrices that are all the
>> same size:
>>
>>
>> http://www.mcs.anl.gov/petsc/petsc-as/snapshots/petsc-current/docs/manualpages/Mat/MatSetValuesBatch.html
>>
>>
>>> - Is it such that matrices are assembled in their entirety on the CPU,
>>> and then copied over to the GPU (after calling MatAssemblyBegin)? Or are
>>> values copied over to the GPU each time you call MatSetValues?
>>>
>>
>> That function assembles the matrix on the GPU and then copies to the CPU.
>> The only time you do not want this copy is when
>> you are running in serial and never touch the matrix afterwards, so I
>> left it in.
>>
>>
>>> - Can we expect to see any speedup from using MatSetValuesBatch over
>>> MatSetValues, or is the batch version simply a utility function? This
>>> question goes for both CPU- and GPU-based matrices.
>>>
>>
>> CPU: no
>>
>> GPU: yes, I see about the memory bandwidth ratio
>>
>>
>> Hi,
>
> I have now integrated MatSetValuesBatch in our existing PETSc wrapper
> layer. I have tested matrix assembly with Poisson's equation on different
> meshes with elements of varying order. I have timed the single call to
> MatSetValuesBatch and compared that to the total time consumed by the
> repeated calls to MatSetValues in the old implementation. I have the
> following results:
>
> Poisson on 1000x1000 unit square, 1st order Lagrange elements:
> MatSetValuesBatch: 0.88576 s
> repeated calls to MatSetValues: 0.76654 s
>
> Poisson on 500x500 unit square, 2nd order Lagrange elements:
> MatSetValuesBatch: 0.9324 s
> repeated calls to MatSetValues: 0.81644 s
>
> Poisson on 300x300 unit square, 3rd order Lagrange elements:
> MatSetValuesBatch: 0.93988 s
> repeated calls to MatSetValues: 1.03884 s
>
> As you can see, the two methods take almost the same amount of time.
> What behavior and performance should we expect? Is there any way to
> optimize the performance of batched assembly?
>

Almost certainly it is not dispatching to the CUDA version. The regular
version just calls MatSetValues() in a loop. Are you
using a SEQAIJCUSP matrix?


> I also have a problem with Thrust throwing std::bad_alloc on some calls to
> MatSetValuesBatch. The exception originates in thrust::device_ptr<void>
> thrust::detail::device::cuda::malloc<0u>(unsigned long). It seems to be
> thrown when the number of double values I send to MatSetValuesBatch
> approaches 30 million. I am testing this on a laptop with 4 GB RAM and a
> GeForce 540 M (1 GB memory), so the 30 million doubles are far away from
> exhausting my memory, both on the host and device side. Any clues on what
> causes this problem and how to avoid it?
>

It uses more memory that just the values. I would have to look at the
specific case, but
I assume that the memory is exhausted.

   Matt


> Thanks,
>
> Fredrik
>
>


-- 
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which their
experiments lead.
-- Norbert Wiener
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